EEG source localization using a sparsity prior based on Brodmann areas
Article
Article Title | EEG source localization using a sparsity prior based on Brodmann areas |
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ERA Journal ID | 36561 |
Article Category | Article |
Authors | Saha, Sajib (Author), Nesterets, Yakov (Author), Rana, Rajib (Author), Tahtali, Murat (Author), de Hoog, Frank (Author) and Gureyev, Timur (Author) |
Journal Title | International Journal of Imaging Systems and Technology |
Journal Citation | 27 (4), pp. 333-344 |
Number of Pages | 12 |
Year | 2017 |
Publisher | John Wiley & Sons |
Place of Publication | United States |
ISSN | 0899-9457 |
1098-1098 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/ima.22236 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/ima.22236 |
Abstract | Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%. |
Keywords | Brodmann map; electroencephalography; inverse problem; source localization; sparse reconstruction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia |
Institute for Resilient Regions | |
University of New South Wales | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q49yz/eeg-source-localization-using-a-sparsity-prior-based-on-brodmann-areas
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